An-Ping Li

Unambiguous identification of Majorana zero modes in topological superconductors remains a challenge due to complex in-gap states that can also produce zero-bias conductance peaks. Here we demonstrate a data-driven workflow that integrates pixel-wise spectral deconvolution with machine learning to analyze tunneling spectroscopy from an intrinsic topological superconductor. Local density of states spectra, acquired with a millikelvin scanning tunneling microscope under magnetic fields, are decomposed into multiple Lorentzian peaks. The extracted peak parameters are assembled into a structured feature set and clustered without supervision. The clustering separates vortices in the superconductor exhibiting zero-bias-peaks consistent with established characteristics of Majorana zero modes from vortices displaying zero-bias-peak-mimicking features of trivial origin. Spatially resolved zero-bias-peak distributions differentiate isotropic vortex cores with well-defined peaks from vortices that exhibit locally distorted peaks. Comparing these distributions to defect locations measured without magnetic field, we find a correlation between local heterogeneity and their formation. This objective and reproducible workflow advances reliable detection of Majorana zero modes, providing a foundation for their manipulation towards quantum computation.

The topological magnet MnBi 2 Te 4 (MBT), with gapped topological surface state, is an attractive platform for realizing quantum anomalous Hall and axion insulator states. However, the experimentally observed surface state gaps fail to meet theoretical predictions, although the exact mechanism behind the gap suppression has been debated. Recent theoretical studies suggest that intrinsic antisite defects push the topological surface state away from the MBT surface, closing its gap and making it less accessible to scanning probe experiments. Here, we report on the local effect of defects on the MBT surface states and demonstrate that high defect concentrations lead to a displacement of the surface states well into the MBT crystal, validating the theorized mechanism. The local and global influence of antisite defects on the topological surface states are studied with samples of varying defect densities by combining scanning tunneling microscopy, angle-resolved photoemission spectroscopy, and density functional theory. Our findings identify a combination of increased defect density and reduced defect spacing as the primary factors underlying the displacement of the surface states and suppression of surface gap, guiding further development of topological quantum materials.

Intercalating a Pb monolayer between graphene and SiC(0001) creates a densely packed metallic layer in close proximity to graphene. Using low-temperature four-point-probe scanning tunneling microscopy and density functional theory, we correlate the local conductivity of this two-dimensional heterostructure with spatially resolved spectroscopy. By varying the tunneling gap, we distinguish the density-of-states contributions of the decoupled graphene sheet and the buried Pb interface layer. At large tip–sample separations, the spectra resemble those of charge-neutral, quasi-freestanding graphene with a small contribution of the metallic Pb layer beneath. This separation confirms the presence of a 5 meV energy gap in graphene, primarily arising from symmetry breaking induced by the epitaxial Pb layer. A proximity-induced intrinsic spin–orbit coupling appears negligible or is compensated by Rashba-type interactions.

The atomically precise engineering of impurities in graphene and the understanding of their structural and carrier-dependent electronic properties at the nanoscale are crucial for advancing graphene-based nanoelectronics, catalysis, and energy technologies. Here, we demonstrate controllable incorporation of the elusive 3-fold-coordinated O substitutions into graphene using low-energy O⁺ ion implantation under ultrahigh-vacuum conditions. By combining high-resolution scanning tunneling microscopy and spectroscopy (STM/S), bond-resolved noncontact atomic force microscopy techniques, and density functional theory (DFT) calculations, we resolve both the structural and electronic properties of the O-related defects. The STM/S measurements, corroborated by DFT calculations, uncover a characteristic impurity state that is energetically pinned to the Dirac point across different charge-carrier doping regimes. Molecular dynamics simulations further reveal the distribution of implantation-induced configurations and identify the formation of 3-fold-coordinated O dopants. This work provides a viable route to incorporate 3-fold-coordinated O dopants and opens new opportunities for controlled defect engineering in graphene.

Unambiguous identification of Majorana zero modes (MZMs) in topological superconductors (TSCs) remains a challenge due to complex in-gap states that can also produce zero-bias conductance peaks (ZBPs). Here we demonstrate a data-driven workflow that integrates pixel-wise spectral deconvolution with machine-learning (ML) to analyze tunneling spectroscopy from FeTe0.55Se0.45, an intrinsic TSC. Based on the local density of states (LDOS) spectra acquired with a millikelvin scanning tunneling microscope under magnetic fields, each spectrum was decomposed into multiple Lorentzian peaks. The extracted peak parameters were assembled into a structured feature set and subsequently embedded and clustered with unsupervised ML algorithms. ML-based clustering identified distinct classes of LDOS spectra, separating superconductor vortices exhibiting ZBPs consistent with established characteristics of MZMs from vortices displaying ZBP-mimicking features of trivial origin. Furthermore, spatially resolved ZBP distributions differentiate isotropic vortex cores with well-defined ZBPs from vortices that exhibit locally distorted ZBPs. By comparing the ZBP distributions to defect locations measured without magnetic field, we found a correlation between local heterogeneity and the ZBP formation, necessitating the systematic, data-driven analysis to disentangle genuine MZM signatures in TSC. This objective and reproducible workflow advances reliable MZM detection in TSCs, providing a foundation for MZM manipulation towards quantum computation.

Manipulating matter with a scanning tunneling microscope (STM) enables the creation of atomically defined artificial structures that host designer quantum states. However, the time-consuming nature of the manipulation process, coupled with the sensitivity of the STM tip, constrains the exploration of diverse configurations and limits the size of the designed features. In this study, we present a reinforcement learning (RL)-based framework for creating artificial structures by spatially manipulating carbon monoxide (CO) molecules on a copper substrate by using the STM tip. The automated workflow combines molecule detection and manipulation, employing deep-learning-based object detection to locate CO molecules and linear assignment algorithms to allocate these molecules to designated target sites. We initially perform molecule maneuvering based on randomized parameter sampling for sample bias, tunneling current set point, and manipulation speed. This data set is then structured into an action trajectory used to train an RL agent. The model is subsequently deployed on the STM for real-time fine-tuning of the manipulation parameters during structure construction. Our approach incorporates path-planning protocols coupled with active drift compensation to enable atomically precise fabrication of structures with significantly reduced human input while realizing larger-scale artificial lattices with the desired electronic properties. Using our approach, we demonstrate the automated construction of an extended artificial graphene lattice and confirm the existence of a characteristic Dirac point in its electronic structure. Further challenges regarding the RL-based structural assembly scalability are discussed.

Spin defects in solids offer promising platforms for quantum sensing and memory due to their long coherence times and compatibility with quantum networks. Here, we integrate a single nitrogen vacancy (NV) center in diamond with scanning probe microscopy to discover, read out, and spatially map arbitrary spin-based quantum sensors at the nanoscale. Using the boron vacancy (V _B ^- ) center in hexagonal boron nitride—an emerging two-dimensional spin system—as a model, we detect its electron spin resonance through changes in the spin relaxation time (T ₁ ) of a nearby NV center, without requiring direct optical excitation or readout of the V _B ^- fluorescence. Cross relaxation between the NV and V _B ^- ensembles results in a pronounced NV T ₁ reduction, enabling nanoscale mapping of spin defect distributions beyond the optical diffraction limit. This approach highlights NV centers as versatile quantum probes for characterizing spin systems, including those emitting at wavelengths beyond the range of silicon-based detectors. Our results open a pathway to hybrid quantum architectures where sensing and readout qubits are decoupled, facilitating the discovery of otherwise inaccessible quantum defects for advanced sensing and quantum networking.